{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "import tensorflow.keras.layers as layers\n",
    "import time as time\n",
    "import tensorflow.keras.preprocessing.image as image\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from scipy.io import loadmat\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing  # 0-1编码\n",
    "from sklearn.model_selection import StratifiedShuffleSplit  # 随机划分，保证每一类比例相同\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.initializers import glorot_uniform\n",
    "import matplotlib.pyplot as plt\n",
    "from prettytable import PrettyTable\n",
    "import six\n",
    "import math\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "from sklearn.metrics import classification_report\n",
    "from matplotlib.font_manager import FontProperties\n",
    "from sklearn.impute import SimpleImputer\n",
    "from einops.layers.tensorflow import Rearrange"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### by Zg,Henan University of Technology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "physical_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "tf.config.experimental.set_memory_growth(physical_devices[0], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train,y_train),(X_test,y_test)=tf.keras.datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotImages(images_arr):\n",
    "    fig, axes = plt.subplots(2, 5, figsize=(20, 10))\n",
    "    axes = axes.flatten()\n",
    "    for img, ax in zip(images_arr, axes):\n",
    "        ax.imshow(img)\n",
    "        ax.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotImages(X_train[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train =  StandardScaler().fit_transform(X_train.astype(np.float32).reshape(-1,1)).reshape(-1,32,32,3)\n",
    "X_test =  StandardScaler().fit_transform(X_test.astype(np.float32).reshape(-1,1)).reshape(-1,32,32,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_ = np.transpose(X_train,(0,3,1,2))\n",
    "X_test_ = np.transpose(X_test,(0,3,1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotImages(X_train[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_ = tf.keras.utils.to_categorical(y_train,10)\n",
    "y_test_ = tf.keras.utils.to_categorical(y_test,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feed_forward_network(d_model, dff):#dff:dim of feed_forward_network\n",
    "    return tf.keras.Sequential([\n",
    "        tf.keras.layers.Dense(dff, activation='relu'),\n",
    "        tf.keras.layers.Dense(d_model)\n",
    "    ])\n",
    "def get_activation(identifier):\n",
    "\n",
    "    if isinstance(identifier, six.string_types):\n",
    "        name_to_fn = {\"gelu\": gelu}\n",
    "        identifier = str(identifier).lower()\n",
    "        if identifier in name_to_fn:\n",
    "            return tf.keras.activations.get(name_to_fn[identifier])\n",
    "    return tf.keras.activations.get(identifier)\n",
    "\n",
    "def gelu(x):\n",
    "\n",
    "    cdf = 0.5 * (1.0 + tf.tanh(\n",
    "        (math.sqrt(2 / math.pi) * (x + 0.044715 * tf.pow(x, 3)))))\n",
    "    return x * cdf\n",
    "# 构造mutil head attention层\n",
    "class MutilHeadAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self, d_model, num_heads,max_lenth): # d_model:model的维度, num_heads:多头的头数\n",
    "        super(MutilHeadAttention, self).__init__()\n",
    "        \n",
    "        self.num_heads = num_heads #属性\n",
    "        self.d_model = d_model\n",
    "        self.scale = d_model ** -0.5\n",
    "        self.max_lenth = max_lenth\n",
    "        self.to_qkv = tf.keras.layers.Dense(d_model * 3, use_bias=False)\n",
    "        self.to_out = tf.keras.layers.Dense(d_model)\n",
    "        \n",
    "        self.rearrange_qkv = Rearrange('batch max_lenth (qkv heads depth) -> qkv batch heads max_lenth depth',\n",
    "                                       qkv = 3, heads = self.num_heads) #(64*3=192),h=8,qkv=3,depth=8\n",
    "        self.rearrange_out = Rearrange('batch heads max_lenth depth -> batch max_lenth (heads depth)')\n",
    "        \n",
    "    def call(self,x):\n",
    "        qkv = self.to_qkv(x)  # batch,max_lenth,d_model --> batch,max_lenth,3*d_model\n",
    "        qkv = self.rearrange_qkv(qkv) # batch,max_lenth,3*d_model --> 3,batch,heads,max_lenth,depth\n",
    "        q = qkv[0] # batch,heads,max_lenth,depth\n",
    "        k = qkv[1] #batch,heads,max_lenth,depth\n",
    "        v = qkv[2] #batch,heads,max_lenth,depth\n",
    "        \n",
    "        dots = tf.einsum('bhid,bhjd->bhij', q, k) * self.scale \n",
    "        attn = tf.nn.softmax(dots,axis=-1)\n",
    "        out = tf.einsum('bhij,bhjd->bhid', attn, v)\n",
    "        out = self.rearrange_out(out) #  batch heads max_lenth depth -> batch max_lenth (heads depth)\n",
    "        output =  self.to_out(out)\n",
    "        return output,attn\n",
    "    \n",
    "class EncoderLayer(keras.layers.Layer):\n",
    "    \"\"\"\n",
    "    x->self attention -> add&normalize & dropout\n",
    "    -> feed_forward -> add& normalize & dropout\n",
    "    \"\"\"\n",
    "    def __init__(self, d_model, num_heads, dff,max_lenth, rate = 0.1):\n",
    "        super(EncoderLayer,self).__init__()\n",
    "        self.mha = MutilHeadAttention(d_model,num_heads,max_lenth) #MutilHeadAttention\n",
    "        self.ffn = feed_forward_network(d_model,dff)#feed forward network\n",
    "        \n",
    "        self.layer_norm1 = keras.layers.LayerNormalization(\n",
    "            epsilon=1e-6)\n",
    "        self.layer_norm2 = keras.layers.LayerNormalization(\n",
    "            epsilon=1e-6)\n",
    "        \n",
    "        self.dropout1 = keras.layers.Dropout(rate)\n",
    "        self.dropout2 = keras.layers.Dropout(rate)\n",
    "    \n",
    "    def call(self,x):\n",
    "        # x.shape:(batch_size,seq_len,dim),dim = d_model\n",
    "        # atten_output.shape:(batch_size,seq_len,d_model)\n",
    "        attn_output,_ = self.mha(x)\n",
    "        attn_output = self.dropout1(attn_output) #training = training\n",
    "        out1 = self.layer_norm1(x + attn_output)\n",
    "        # ffn.shape:(batch_size,seq_len,dim),dim = d_model\n",
    "        # out2.shape:(batch_size,seq_len,d_model)\n",
    "        ffn_output = self.ffn(out1)\n",
    "        ffn_output = self.dropout2(ffn_output) # training = training\n",
    "        out2 = self.layer_norm2(out1 + ffn_output)\n",
    "        \n",
    "        return out2\n",
    "    \n",
    "class EncoderModel(tf.keras.Model):\n",
    "    def __init__(self, num_layers, max_lenth,num_classes,d_model,num_heads,dff,dense_dim,\n",
    "                 image_size,patch_size,channels=3,rate = 0.1):\n",
    "        super(EncoderModel,self).__init__()\n",
    "        self.d_model = d_model\n",
    "        self.num_layers = num_layers\n",
    "        self.max_lenth = max_lenth\n",
    "        self.embedding = keras.layers.Dense(d_model)\n",
    "        \n",
    "        assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'\n",
    "        num_patches = (image_size // patch_size) ** 2\n",
    "        patch_dim = channels * patch_size ** 2\n",
    "        self.patch_size = patch_size\n",
    "        self.rearrange = Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=self.patch_size, p2=self.patch_size)\n",
    "        \n",
    "        self.dim = d_model\n",
    "        \n",
    "        self.patch_to_embedding = tf.keras.layers.Dense(d_model)\n",
    "        self.pos_embedding = self.add_weight(\" \",\n",
    "                                             shape=[num_patches + 1,\n",
    "                                                    d_model],\n",
    "                                             initializer=tf.keras.initializers.RandomNormal(),\n",
    "                                             dtype=tf.float32)\n",
    "        \n",
    "        self.cls_token = self.add_weight(\"cls_token\",\n",
    "                                         shape=[1,\n",
    "                                                1,\n",
    "                                                d_model],\n",
    "                                         initializer=tf.keras.initializers.RandomNormal(),\n",
    "                                         dtype=tf.float32)\n",
    "        \n",
    "        self.dropout = keras.layers.Dropout(rate)\n",
    "        self.encoder_layers =[\n",
    "            EncoderLayer(d_model,num_heads,dff,max_lenth,rate) \n",
    "            for _ in range(self.num_layers)]\n",
    "        \n",
    "        self.mlp_head = tf.keras.Sequential([tf.keras.layers.Dense(dense_dim, activation=get_activation('gelu')),\n",
    "                                             tf.keras.layers.Flatten(),\n",
    "                                             tf.keras.layers.Dense(num_classes,activation=\"softmax\")])\n",
    "        \n",
    "    @tf.function\n",
    "    def call (self, x): # training = True,encoder_padding_mask = False\n",
    "        # x.shape:(batch_size,input_seq_len)\n",
    "        shapes = tf.shape(x)\n",
    "        x = self.rearrange(x)\n",
    "        x = self.patch_to_embedding(x)\n",
    "        \n",
    "        cls_tokens = tf.broadcast_to(self.cls_token,(shapes[0],1,self.dim))\n",
    "        x = tf.concat((cls_tokens, x), axis=1)\n",
    "        x += self.pos_embedding\n",
    "        x = self.embedding(x)\n",
    "\n",
    "        x = self.dropout(x)\n",
    "        \n",
    "        for i in range(self.num_layers):\n",
    "            x = self.encoder_layers[i](x)\n",
    "        x = self.mlp_head(x)   \n",
    "            # x.shape:(batch_size,input_seq_len,d_model)\n",
    "        return x   \n",
    "    \n",
    "callbacks = [tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', patience=2, \n",
    "                                                  factor=0.7, \n",
    "                                                  min_lr=0.000001)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "patch_size = 2\n",
    "heads = 8\n",
    "num_layers=3\n",
    "max_lenth=256\n",
    "num_classes=10\n",
    "d_model=64\n",
    "num_heads=8\n",
    "dff=128\n",
    "dense_dim=64\n",
    "image_size=32\n",
    "patch_size=2\n",
    "channels=3\n",
    "rate = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8000, 10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_[:8000].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_MHA = EncoderModel(num_layers, max_lenth,num_classes,d_model,num_heads,dff,dense_dim,\n",
    "                         image_size,patch_size,channels=3,rate = 0.1)\n",
    "model_MHA.compile(optimizer='Adam', loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "\n",
    "history_MHA = model_MHA.fit(x=X_train_, y=y_train_, batch_size=256,\n",
    "                            epochs=30,verbose=1, validation_data=(X_test_[:8000], y_test_[:8000]), \n",
    "                            shuffle=True,callbacks=callbacks)#,callbacks=callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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